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Title: TuCCompi: a multi-layer model for distributed heterogeneous computing with tuning capabilities
Authors: Ortega Arranz, Hector
Torres, yuri
Llanos, Diego R.
González Escribano, Arturo
Issue Date: 2015
Publisher: Springer
Description: Producción Científica
Citation: International Journal of Parallel Programming, Vol. 43, Issue 5 (2015), pages 939-960, ISSN 0885-7458
Abstract: During the last decade, parallel processing architectures have become a powerful tool to deal with massively-parallel problems that require High Performance Computing (HPC). The last trend of HPC is the use of heterogeneous environments, that combine different computational processing devices, such as CPU-cores and GPUs (Graphics Processing Units). Maximizing the performance of any GPU parallel implementation of an algorithm requires an in-depth knowledge about the GPU underlying architecture, becoming a tedious manual effort only suited for experienced programmers. In this paper, we present TuCCompi, a multi-layer abstract model that simplifies the programming on heterogeneous systems including hardware accelerators, by hiding the details of synchronization, deployment, and tuning. TuCCompi chooses optimal values for their configuration parameters using a kernel characterization provided by the programmer. This model is very useful to tackle problems characterized by independent, high computational-load independent tasks, such as embarrassingly-parallel problems. We have evaluated TuCCompi in different, real-world, heterogeneous environments using the All-Pair Shortest-Path problem as a case study.
Peer Review: SI
DOI: 10.1007/s10766-015-0349-6
Sponsor: Ministerio de Economía y Competitividad and ERDF program of the European Union: CAPAP-H5 network (TIN2014-53522-REDT), MOGECOPP project (TIN2011-25639); Junta de Castilla y Leon (Spain): ATLAS project (VA172A12-2); and the COST Program Action IC1305: NESUS.
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Rights Owner: Springer
Language: eng
Rights: info:eu-repo/semantics/restrictedAccess
Appears in Collections:DEP41 - Artículos de revista

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